Unmanned aerial vehicle nest system for auxiliary positioning

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses an auxiliary positioning unmanned aerial vehicle nest system which comprises an autonomous control system used for controlling an automatic system of the whole nest system, and is responsible for monitoring and adjusting the position and attitude of an unmanned aerial vehicle and managing landing, charging and take-off processes at the same time. The task scheduling module is used for determining the priority of tasks in the communication process, the communication and data transmission module is used for realizing information exchange between the airport nest system and the unmanned aerial vehicle, and the network compensation module is used for solving the problems of data transmission, communication delay and time sequence synchronization among different networking. And the off-network communication module is used for taking a response when the communication connection with the base station is lost. Through the arrangement of a task priority allocation algorithm and a dynamic scheduling strategy unit in the task scheduling module, emergency processing of the task can be performed according to the emergency degree of the task, the emergency task can be processed in time, and then economic loss can be avoided or other larger loss can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an auxiliary positioning unmanned aerial vehicle nest system. Background Art

[0002] A drone nest system usually refers to automated equipment used for drone takeoff and landing, charging, maintenance or storage. In drone application scenarios, a drone nest system with assisted positioning may refer to a system used to help drones accurately locate, land and take off.

[0003] The drone nest system with auxiliary positioning in the prior art can only execute tasks in the order of the assigned tasks. However, in the process of executing the tasks, some emergencies are inevitable. However, since the drone can only execute in the order of the tasks, the emergency tasks cannot be completed on time, which can easily cause economic losses or other greater losses. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an auxiliary positioning UAV nest system, which solves the problem that the existing UAV nest system can only execute tasks according to the assigned task sequence, resulting in emergency tasks cannot be completed on time, which easily causes economic losses or other greater losses.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an auxiliary positioning drone nest system, comprising:

[0006] The autonomous control system is used to control the automation system of the entire machine nest system, responsible for monitoring and adjusting the position and attitude of the drone, and managing the landing, charging and take-off processes;

[0007] The task scheduling module is used to determine the priority of tasks during the communication process;

[0008] The communication and data transmission module is used to realize information exchange between the machine nest system and the UAV;

[0009] The network compensation module is used to handle data transmission, communication delay and timing synchronization issues between different networks;

[0010] The off-grid communication module is used to take countermeasures when the communication connection with the base station is lost;

[0011] The positioning module is used to obtain the accurate position and attitude information of the drone;

[0012] The automatic guidance and landing module is used to identify the landing area, track the drone, and provide it with precise guidance and landing;

[0013] The charging module is used to automatically charge the drone battery.

[0014] Preferably, the task scheduling module includes a task priority allocation algorithm, a dynamic scheduling strategy unit, an event-triggered scheduling algorithm, a user-set parameter unit and a machine intelligence algorithm. The task priority allocation algorithm is used to assign priorities according to the nature, urgency and importance of the task. The dynamic scheduling strategy unit is used to make adjustments according to actual conditions during runtime. The event-triggered scheduling algorithm is used to adjust the priority of the task after an emergency occurs. The user-set parameter unit is used for the user to set the priority or weight for a specific task to meet user needs. The machine intelligence algorithm is used to learn and predict the priority of the task based on historical data and real-time conditions.

[0015] Preferably, the network compensation module includes a timing synchronization unit, a communication delay compensation unit, a data calibration and alignment unit, a fault-tolerant and redundant processing unit, a load balancing unit and an adaptive algorithm. The timing synchronization unit is used to ensure that the data transmission timing between different networks is consistent. The communication delay compensation unit is used to introduce a delay compensation mechanism to coordinate the reception and transmission timing of data. The data calibration and alignment unit is used to perform data calibration and alignment. The fault-tolerant and redundant processing unit is used to ensure the stability and reliability of the system. The load balancing unit is used to balance the communication load between different networks. The adaptive algorithm is used to adjust the compensation strategy according to real-time conditions and network conditions.

[0016] Preferably, the off-grid communication module includes a localization and autonomous navigation unit, a pre-stored map and path planning unit, an autonomous decision-making and emergency processing unit, a data storage and post-processing unit, an autonomous charging unit and a periodic reporting and status update unit. The localization and autonomous navigation unit is used to use local sensor data for positioning and navigation in an off-grid situation. The pre-stored map and path planning unit is used to use pre-stored map data for path planning and navigation. The autonomous decision-making and emergency processing unit is used to have a certain autonomous decision-making ability in an offline situation. The data storage and post-processing unit is used when the drone is offline, so that the drone can store data in a local device. The autonomous charging unit is used when the battery power is low in an offline situation, so that the drone can have the ability to autonomously return to a charging station or charging device. The periodic reporting and status update unit is used when the drone re-establishes a communication connection with the base station. The system may need to periodically report its own status and upload mission data.

[0017] Preferably, the positioning module includes a sensor module, a data processing unit, a position solving algorithm, map framework data, a calibration unit and a user interface unit. The sensor module is used to collect environmental data, object position, speed, and direction information; the data processing unit is responsible for receiving, processing and analyzing data provided by the sensor unit; the position solving algorithm is used to convert the data collected by the sensor into actual position information; the map framework data is used for positioning and navigation; the calibration unit is used to ensure the precision and accuracy of the sensor; and the user interface unit is used to display or communicate to the user.

[0018] Preferably, the sensor module includes a global positioning system, an inertial measurement unit, a lidar, a visual sensor, an ultrasonic sensor, a magnetometer, an infrared sensor and a barometric pressure sensor, the global positioning system is used to receive signals from satellites, the inertial measurement unit is used to measure and track the acceleration, angular velocity and direction of the device, the lidar is used to measure the distance and shape of an object, the visual sensor is used to capture images or videos in the environment, the ultrasonic sensor is used to measure the distance between the object and the sensor, the magnetometer is used to measure the earth's magnetic field, the infrared sensor is used to detect the heat of the object, and the barometric pressure sensor is used for altitude measurement and height positioning.

[0019] Preferably, the automatic guidance and landing module includes an autopilot system, an instrument landing system, a radar altimeter, an automatic landing system, a ground control station, a landing sensor and a fault detection and fault tolerance unit, wherein the autopilot system is used to use navigation data to control the heading, pitch and roll of the aircraft to ensure that it flies along a predetermined path, the instrument landing system is used to provide the horizontal and vertical guidance required for the aircraft during the landing phase, the radar altimeter is used to measure the height of the aircraft from the ground to ensure that the landing begins at an appropriate height, the automatic landing system is used to ensure that the aircraft lands in a safe landing area, the ground control station is used to monitor and adjust the operation of the automatic guidance and landing system, the landing sensor is used to detect obstacles in the landing area, and the fault detection and fault tolerance unit is used to monitor the status of the system, detect potential faults, and take measures to tolerate or correct these faults.

[0020] Preferably, the charging module includes a charging controller, a power management unit, a battery management system, a current sensor, a voltage sensor, a temperature sensor and a charging protection unit, the charging controller is responsible for managing the charging process, the power management unit is used to monitor and adjust the power supply, the battery management system is used to monitor the battery status and balance the charge of each battery in the battery pack, the current sensor is used to measure the charging current, the voltage sensor is used to monitor the battery voltage, the temperature sensor is used to ensure that the charging system does not overheat, and the charging protection unit is used to prevent overcharging, over-discharging and overcurrent.

[0021] Preferably, the autonomous control system, task scheduling module, communication and data transmission module, network compensation module, off-grid communication module, positioning module, automatic guidance and landing module and charging module are command-transmitted and controlled via a control interface.

[0022] Preferably, information is transmitted and interacted between the autonomous control system, task scheduling module, communication and data transmission module, network compensation module, off-grid communication module, positioning module, automatic guidance and landing module and charging module via a data interface.

[0023] The present invention provides a drone nest system for auxiliary positioning, which has the following beneficial effects:

[0024] 1. The present invention can perform emergency processing of tasks according to the urgency of the tasks through the setting of the task priority allocation algorithm and the dynamic scheduling strategy unit in the task scheduling module, so that emergency tasks can be processed in time, thereby avoiding economic losses or other greater losses.

[0025] 2. The present invention can pre-download maps and path planning units through an offline communication module, so that after going offline, it can autonomously respond and temporarily handle according to the autonomous decision-making and emergency processing units, and is equipped with an autonomous charging unit to prevent the occurrence of insufficient power. Therefore, the drone can handle offline situations and complete tasks offline, thereby increasing the practicality of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a framework diagram of the present invention;

[0027] Figure 2 This is a framework diagram of the task scheduling module in the present invention;

[0028] Figure 3 This is a framework diagram of the network compensation module in the present invention;

[0029] Figure 4 This is a framework diagram of the off-grid communication module of the present invention;

[0030] Figure 5 This is a framework diagram of the positioning module in the present invention;

[0031] Figure 6 This is a sensor module framework diagram of the present invention;

[0032] Figure 7 This is a framework diagram of the automatic guidance and landing module in the present invention;

[0033] Figure 8 This is a framework diagram of the charging module in the present invention.

[0034] Among them, 1. Autonomous control system; 2. Task scheduling module; 201. Task priority allocation algorithm; 202. Dynamic scheduling strategy unit; 203. Scheduling algorithm based on event triggering; 204. User-set parameter unit; 205. Machine intelligence algorithm; 3. Communication and data transmission module; 4. Network compensation module; 401. Timing synchronization unit; 402. Communication delay compensation unit; 403. Data calibration and alignment unit; 404. Fault tolerance and redundancy processing unit; 405. Load balancing unit; 406. Adaptive algorithm; 5. Off-grid communication module; 501. Localization and autonomous navigation unit; 502. Pre-stored map and path planning unit; 503. Autonomous decision-making and emergency processing unit; 504. Data storage and post-processing unit; 505. Autonomous charging unit; 506. Periodic reporting and status update unit; 6. Positioning module; 601. Sensor module; 6011. Global Positioning System; 6012, Inertial Measurement Unit; 6013, LiDAR; 6014, Visual Sensor; 6015, Ultrasonic Sensor; 6016, Magnetometer; 6017, Infrared Sensor; 6018, Air Pressure Sensor; 602, Data Processing Unit; 603, Position Solution Algorithm; 604, Map Framework Data; 605, Calibration Unit; 606, User Interface Unit; 7, Automatic Guidance and Landing Module; 701, Autopilot System; 702, Instrument Landing System; 703, Radar Altimeter; 704, Automatic Landing System; 705, Ground Control Station; 706, Landing Sensor; 707, Fault Detection and Fault Tolerance Unit; 8, Charging Module; 801, Charging Controller; 802, Power Management Unit; 803, Battery Management System; 804, Current Sensor; 805, Voltage Sensor; 806, Temperature Sensor; 807, Charging Protection Unit. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example:

[0037] Please see attached Figure 1 -Attached Figure 8 The embodiment of the present invention provides a drone nest system for auxiliary positioning, including:

[0038] The autonomous control system 1 is used to control the automation system of the entire machine nest system, responsible for monitoring and adjusting the position and attitude of the drone, and managing the landing, charging and take-off processes.

[0039] The task scheduling module 2 is used to determine the priority of tasks in the communication process. The task scheduling module 2 includes a task priority allocation algorithm 201, a dynamic scheduling strategy unit 202, an event-triggered scheduling algorithm 203, a user-set parameter unit 204, and a machine intelligence algorithm 205. The task priority allocation algorithm 201 is used to allocate priorities according to the nature, urgency, and importance of the task, and includes two algorithm formulas:

[0040] Static priority assignment, each task is assigned a static priority. This priority is usually specified when the task is created and does not change over time;

[0041] Priority = weight 1 × factor 1 + weight 2 × factor 2 + ... + weight n × factor n;

[0042] The weight indicates the importance of each factor, and the factors may include the urgency, importance, type, and execution time of the task.

[0043] Dynamic priority assignment: In dynamic priority assignment, the priority of tasks may change with time or changes in the environment;

[0044] Priority = f(urgency, importance, remaining execution time, elapsed execution time)

[0045] Where f() is a function, which may be a linear function, exponential function or other algorithm, used to dynamically calculate the priority according to the task attributes;

[0046] The dynamic scheduling strategy unit 202 is used to make adjustments according to actual conditions during operation, including adjusting the priority of tasks according to dynamic factors such as the power status of the drone, the quality of the communication link, and the length of the task queue. The event-triggered scheduling algorithm 203 is used to adjust the priority of tasks after an emergency occurs. For example, an emergency occurs, the drone's power is too low, the communication link is unstable, and other situations may trigger the adjustment of the task priority. The user-set parameter unit 204 is used for the user to set the priority or weight for a specific task to meet user needs. The machine intelligence algorithm 205 is used to learn and predict the priority of tasks based on historical data and real-time conditions. The machine intelligence algorithm 205 includes;

[0047] Linear regression formula: (y=mx+b)

[0048] It is used to model and predict the linear relationship between data, where y is the target variable, x is the input variable, m is the slope, and b is the intercept.

[0049] Reinforcement Learning Algorithms:

[0050] Q-learning update rule: (Q(s,a)=(1-\alpha)\cdotQ(s,a)+\alpha\cdot[r+\gamma\cdot\maxQ(s',a')])

[0051] Where (Q(s,a)) is the value of taking action (a) in state (s), (\alpha) is the learning rate, (r) is the reward, (\gamma) is the discount factor, and (s') and (a') are the next state and action.

[0052] The communication and data transmission module 3 is used to enable information exchange between the machine nest system and the drone, including Bluetooth, 4 / 5G, communication base station, or other communication technologies.

[0053] The network compensation module 4 is used to handle the problems of data transmission, communication delay and timing synchronization between different networks. The network compensation module 4 includes a timing synchronization unit 401, a communication delay compensation unit 402, a data calibration and alignment unit 403, a fault-tolerant and redundant processing unit 404, a load balancing unit 405 and an adaptive algorithm 406. The timing synchronization unit 401 is used to ensure that the data transmission timing between different networks is consistent, and to ensure that the devices in each network can work on the same time reference. The communication delay compensation unit 402 is used to introduce a delay compensation mechanism to coordinate the receiving and sending timing of data. The data calibration and alignment unit 403 is used to perform data calibration and alignment. The fault-tolerant and redundant processing unit 404 is used to ensure the stability and reliability of the system, introduce a fault-tolerant mechanism and redundant processing to cope with possible communication failures or data loss. The load balancing unit 405 is used to balance the communication load between different networks to prevent a certain network from being overloaded. The adaptive algorithm 406 is used to adjust the compensation strategy according to real-time conditions and network conditions. The adaptive algorithm 406 includes:

[0054] Adaptive filtering: Using the Kalman filter formula:

[0055] [x_{k|k-1}=A\cdotx_{k-1|k-1}+B\cdotu_k]

[0056] [P_{k|k-1}=A\cdotP_{k-1|k-1}\cdotA^T+Q]

[0057] [K_k=P_{k|k-1}\cdotH^T\cdot(H\cdotP_{k|k-1}\cdotH^T+R)^{-1}]

[0058] [x_{k|k}=x_{k|k-1}+K_k\cdot(z_k-H\cdotx_{k|k-1})]

[0059] [P_{k|k}=(1-K_k\cdotH)\cdotP_{k|k-1}];

[0060] Where, (x) is the state vector, (A) is the state transfer matrix, (B) is the input matrix, (u) is the input vector, (H) is the observation matrix, (z) is the observation vector, (Q) is the process noise covariance matrix, (R) is the measurement noise covariance matrix, and (P) is the state covariance matrix;

[0061] Adaptive control: [u=-F\cdotx+r]

[0062] [\dot{x}=-\Gamma\cdot\Phi^T\cdote]

[0063] [F = \Phi\cdotP]

[0064] [\dot{P}=\alpha\cdot\Phi\cdot\Phi^T-\beta\cdotP]

[0065] [\dot{\Gamma}=\gamma\cdote^2];

[0066] Where, (u) is the control input, (y) is the system output, (r) is the reference model output, (e) is the tracking error, (x) is the adaptive parameter, and (F) is the adaptive gain matrix;

[0067] Gradient descent with adaptive learning rate:

[0068] [W_{t+1}=W_t-\eta\cdot\frac{\partialJ}{\partialW}];

[0069] Where (W) is the neural network weight, (J) is the loss function, and (\eta) is the learning rate.

[0070] The off-grid communication module 5 is used to take countermeasures when the communication connection with the base station is lost. The off-grid communication module 5 includes a localization and autonomous navigation unit 501, a pre-stored map and path planning unit 502, an autonomous decision-making and emergency processing unit 503, a data storage and post-processing unit 504, an autonomous charging unit 505 and a periodic reporting and status updating unit 506. The localization and autonomous navigation unit 501 is used to use local sensor data for positioning and navigation in an off-grid situation. The pre-stored map and path planning unit 502 is used to use pre-stored map data for path planning and navigation. The autonomous decision-making and emergency processing unit 503 is used to have a certain autonomous decision-making ability in an offline situation. The data storage and post-processing unit 504 is used when the drone is offline. The data can be stored in the local device. The autonomous charging unit 505 is used when the battery power is low in an offline situation. The drone can have the ability to return to the charging station or charging equipment autonomously. The periodic reporting and status updating unit 506 is used when the drone re-establishes a communication connection with the base station. The system may need to periodically report its own status and upload mission data.

[0071] The positioning module 6 is used to obtain the accurate position and attitude information of the drone. The positioning module 6 includes a sensor module 601, a data processing unit 602, a position solution algorithm 603, map framework data 604, a calibration unit 605 and a user interface unit 606. The sensor module 601 is used to collect environmental data, object position, speed, and direction information. The data processing unit 602 is responsible for receiving, processing and analyzing the data provided by the sensor unit. The position solution algorithm 603 is used to convert the data collected by the sensor into actual position information;

[0072] Position solution algorithm 603 includes: Triangulation method:

[0073] [d=\sqrt{(x-x_0)^2+(y-y_0)^2+(z-z_0)^2}]

[0074] Where (d) is the distance, ((x_0,y_0,z_0)) is the coordinate of the reference point, and ((x,y,z)) is the coordinate of the object to be determined.

[0075] Least squares formula: [X = (A^TA)^{-1}A^TB];

[0076] Where (X) is the position estimate, (A) contains the relative position information, and (B) contains the known position of each agent;

[0077] State estimation formula: [x_k=f(x_{k-1},u_k)+w_k][z_k=h(x_k)+v_k];

[0078] Where (x_k) is the state, (u_k) is the input, (w_k) is the process noise, (z_k) is the measurement, and (v_k) is the measurement noise.

[0079] The map framework data 604 is used for positioning and navigation, the calibration unit 605 is used to ensure the precision and accuracy of the sensor, and the user interface unit 606 is used to display or communicate to the user;

[0080] The sensor module 601 includes a global positioning system 6011, an inertial measurement unit 6012, a lidar 6013, a visual sensor 6014, an ultrasonic sensor 6015, a magnetometer 6016, an infrared sensor 6017 and a pressure sensor 6018. The global positioning system 6011 is used to receive signals from satellites, the inertial measurement unit 6012 is used to measure and track the acceleration, angular velocity and direction of the device, the lidar 6013 is used to measure the distance and shape of an object, the visual sensor 6014 is used to capture images or videos in the environment, the ultrasonic sensor 6015 is used to measure the distance between the object and the sensor, the magnetometer 6016 is used to measure the earth's magnetic field, the infrared sensor 6017 is used to detect the heat of the object, and the pressure sensor 6018 is used for altitude measurement and height positioning.

[0081] The automatic guidance and landing module 7 is used to identify the landing area, track the UAV, and provide it with precise guidance and landing. The automatic guidance and landing module 7 includes an autopilot system 701, an instrument landing system 702, a radar altimeter 703, an automatic landing system 704, a ground control station 705, a landing sensor 706, and a fault detection and fault tolerance unit 707. The autopilot system 701 is used to use navigation data to control the heading, pitch, and roll of the aircraft to ensure that it flies along a predetermined path. The instrument landing system 702 is used to provide the horizontal and vertical guidance required for the aircraft during the landing phase. The radar altimeter 703 is used to measure the height of the aircraft from the ground to ensure that the landing begins at an appropriate height. The automatic landing system 704 is used to ensure that the aircraft lands in a safe landing area. The ground control station 705 is used to monitor and adjust the operation of the automatic guidance and landing system. The landing sensor 706 is used to detect obstacles in the landing area. The fault detection and fault tolerance unit 707 is used to monitor the status of the system, detect potential faults, and take measures to tolerate or correct these faults.

[0082] The charging module 8 is used to automatically charge the drone battery. The charging module 8 includes a charging controller 801, a power management unit 802, a battery management system 803, a current sensor 804, a voltage sensor 805, a temperature sensor 806 and a charging protection unit 807. The charging controller 801 is responsible for managing the charging process, the power management unit 802 is used to monitor and adjust the power supply, the battery management system 803 is used to monitor the battery status and balance the charge of each battery in the battery pack, the current sensor 804 is used to measure the charging current, the voltage sensor 805 is used to monitor the battery voltage, the temperature sensor 806 is used to ensure that the charging system is not overheated, and the charging protection unit 807 is used to prevent overcharging, over-discharging and overcurrent.

[0083] Please see attached Figure 1 The autonomous control system 1, the task scheduling module 2, the communication and data transmission module 3, the network compensation module 4, the off-grid communication module 5, the positioning module 6, the automatic guidance and landing module 7 and the charging module 8 perform command transmission and control through the control interface, and the autonomous control system 1, the task scheduling module 2, the communication and data transmission module 3, the network compensation module 4, the off-grid communication module 5, the positioning module 6, the automatic guidance and landing module 7 and the charging module 8 perform information transmission and interaction through the data interface.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An auxiliary positioning UAV nest system, characterized in that: include: Autonomous control system (1) is an automation system used to control the entire nest system, responsible for monitoring and adjusting the position and attitude of the drone, and managing the landing, charging and take-off processes; The task scheduling module (2) is used to determine the priority of tasks during the communication process; The communication and data transmission module (3) is used to realize information exchange between the machine nest system and the drone; The network compensation module (4) is used to handle the problems of data transmission, communication delay and timing synchronization between different networks; The off-grid communication module (5) is used to take countermeasures when the communication connection with the base station is lost; The positioning module (6) is used to obtain the accurate position and attitude information of the drone; The automatic guidance and landing module (7) is used to identify the landing area, track the UAV, and provide it with precise guidance and landing; The charging module (8) is used to automatically charge the drone battery.

2. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The task scheduling module (2) comprises a task priority allocation algorithm (201), a dynamic scheduling strategy unit (202), an event-triggered scheduling algorithm (203), a user-set parameter unit (204) and a machine intelligence algorithm (205), wherein the task priority allocation algorithm (201) is used to allocate priorities according to the nature, urgency and importance of tasks, the dynamic scheduling strategy unit (202) is used to make adjustments according to actual conditions during operation, the event-triggered scheduling algorithm (203) is used to adjust the priority of tasks after an emergency occurs, the user-set parameter unit (204) is used for users to set priorities or weights for specific tasks to meet user needs, and the machine intelligence algorithm (205) is used to learn and predict the priority of tasks according to historical data and real-time conditions.

3. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The network compensation module (4) comprises a timing synchronization unit (401), a communication delay compensation unit (402), a data calibration and alignment unit (403), a fault tolerance and redundancy processing unit (404), a load balancing unit (405) and an adaptive algorithm (406), wherein the timing synchronization unit (401) is used to ensure that the data transmission timing between different networks is consistent, the communication delay compensation unit (402) is used to introduce a delay compensation mechanism to coordinate the receiving and sending timing of data, the data calibration and alignment unit (403) is used to perform data calibration and alignment, the fault tolerance and redundancy processing unit (404) is used to ensure the stability and reliability of the system, the load balancing unit (405) is used to balance the communication load between different networks, and the adaptive algorithm (406) is used to adjust the compensation strategy according to real-time conditions and network conditions.

4. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The off-grid communication module (5) comprises a localization and autonomous navigation unit (501), a pre-stored map and path planning unit (502), an autonomous decision-making and emergency processing unit (503), a data storage and post-processing unit (504), an autonomous charging unit (505) and a periodic reporting and status updating unit (506). The localization and autonomous navigation unit (501) is used to use local sensor data for positioning and navigation in an off-grid situation. The pre-stored map and path planning unit (502) is used to use pre-stored map data for path planning and navigation. The autonomous decision-making and emergency processing unit (503) is used to have a certain autonomous decision-making ability in an offline situation. The data storage and post-processing unit (504) is used to enable the drone to store data in a local device when it is offline. The autonomous charging unit (505) is used to enable the drone to have the ability to autonomously return to a charging station or charging device when the battery power is low in an offline situation. The periodic reporting and status updating unit (506) is used to enable the system to periodically report its own status and upload mission data when the drone re-establishes a communication connection with the base station.

5. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The positioning module (6) comprises a sensor module (601), a data processing unit (602), a position solving algorithm (603), map framework data (604), a calibration unit (605) and a user interface unit (606), wherein the sensor module (601) is used to collect environmental data, object position, speed and direction information, the data processing unit (602) is responsible for receiving, processing and analyzing data provided by the sensor unit, the position solving algorithm (603) is used to convert the data collected by the sensor into actual position information, the map framework data (604) is used for positioning and navigation, the calibration unit (605) is used to ensure the precision and accuracy of the sensor, and the user interface unit (606) is used to display or convey to the user.

6. The drone nest system for auxiliary positioning according to claim 5, characterized in that: The sensor module (601) includes a global positioning system (6011), an inertial measurement unit (6012), a laser radar (6013), a visual sensor (6014), an ultrasonic sensor (6015), a magnetometer (6016), an infrared sensor (6017) and an air pressure sensor (6018), wherein the global positioning system (6011) is used to receive signals from a satellite, the inertial measurement unit (6012) is used to measure and track the acceleration, angular velocity and direction of the device, the laser radar (6013) is used to measure the distance and shape of an object, the visual sensor (6014) is used to capture images or videos in the environment, the ultrasonic sensor (6015) is used to measure the distance between the object and the sensor, the magnetometer (6016) is used to measure the earth's magnetic field, the infrared sensor (6017) is used to detect the heat of an object, and the air pressure sensor (6018) is used to measure altitude and height positioning.

7. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The automatic guidance and landing module (7) comprises an autopilot system (701), an instrument landing system (702), a radar altimeter (703), an automatic landing system (704), a ground control station (705), a landing sensor (706) and a fault detection and fault tolerance unit (707). The autopilot system (701) is used to use navigation data to control the heading, pitch and roll of the aircraft to ensure that it flies along a predetermined path. The instrument landing system (702) is used to provide the horizontal and vertical guidance required by the aircraft during the landing phase. The radar altimeter (703) is used to measure the height of the aircraft from the ground to ensure that the landing begins at an appropriate height. The automatic landing system (704) is used to ensure that the aircraft lands in a safe landing area. The ground control station (705) is used to monitor and adjust the operation of the automatic guidance and landing system. The landing sensor (706) is used to detect obstacles in the landing area. The fault detection and fault tolerance unit (707) is used to monitor the status of the system, detect potential faults, and take measures to tolerate or correct these faults.

8. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The charging module (8) comprises a charging controller (801), a power management unit (802), a battery management system (803), a current sensor (804), a voltage sensor (805), a temperature sensor (806) and a charging protection unit (807), wherein the charging controller (801) is responsible for managing the charging process, the power management unit (802) is used to monitor and adjust the power supply, the battery management system (803) is used to monitor the battery status and balance the charge of each battery in the battery pack, the current sensor (804) is used to measure the charging current, the voltage sensor (805) is used to monitor the battery voltage, the temperature sensor (806) is used to ensure that the charging system is not overheated, and the charging protection unit (807) is used to prevent overcharging, overdischarging and overcurrent.

9. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The autonomous control system (1), the task scheduling module (2), the communication and data transmission module (3), the network compensation module (4), the off-grid communication module (5), the positioning module (6), the automatic guidance and landing module (7) and the charging module (8) are connected through a control interface for command transmission and control.

10. The drone nest system for auxiliary positioning according to claim 1, characterized in that: The autonomous control system (1), the task scheduling module (2), the communication and data transmission module (3), the network compensation module (4), the off-grid communication module (5), the positioning module (6), the automatic guidance and landing module (7) and the charging module (8) transmit information and interact with each other via a data interface.

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